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Experimenting with artificial neural networks-artificial intelligence mini-tutorial. 3

机译:尝试人工神经网络-人工智能小型教程。 3

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For pt.1 see ibid., p.33-42; for pt.2 see ibid., p.43-4. To show how neural nets work, experiences in an experiment using them are described. The experiment involves using AI techniques to assist in the discovery of causal relationships between the variables existing in a large clinical trial database. A peripheral vascular disease database was used to acquire a feeling for the complexities involved in developing a distributed encoding scheme and to determine the computational resources required to train a neural net for the type of data used. By testing several models the effects of changes in the encoding scheme and the number of training iterations the system needed to predict the appropriate change needed could be determined. These results were compared to the information available from other analyses of the same data. The generative capabilities of the system were then tested by training it over one sample of cases and applying it to cases it had not encountered before. Some idea of the computational resources needed in terms of time and memory capacity was developed.
机译:关于第1点,请参见同上,第33-42页;关于第2点,请参见同上,第43-4页。为了显示神经网络如何工作,描述了使用它们的实验经验。该实验涉及使用AI技术来帮助发现大型临床试验数据库中存在的变量之间的因果关系。周围血管疾病数据库用于获得开发分布式编码方案所涉及的复杂性的感觉,并确定为使用的数据类型训练神经网络所需的计算资源。通过测试几个模型,编码方案中的变化的影响以及训练迭代的次数,可以确定预测所需的适当变化所需的系统。将这些结果与从相同数据的其他分析中获得的信息进行了比较。然后通过对一个案例样本进行培训并将其应用于以前从未遇到过的案例来测试系统的生成能力。对时间和存储容量方面所需的计算资源提出了一些想法。

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